Little is known about effective ways to operationalize agricultural innovation processes. The authors of this article use the MasAgro program in Mexico (which aims to increase maize and wheat productivity, profitability and sustainability), and the experiences of middle level ‘hub managers’, to understand how innovation processes occur in heterogeneous and changing contexts. Their research shows how a program, that initially had a relatively narrow technology focus, evolved towards an innovation system approach.

The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.

This study aims to explore how the Positive Deviance approach can be adapted to identify and prioritize rural development interventions for diverse farming households that pursue multiple objectives. We describe the adapted approach, consisting of three research steps, and a case study implementation in Tanzania. Based on this experience,  the potential of the Positive Deviance approach for household-specific prioritization of multi-objective development opportunities is discussed

The aim of this study was to identify the actors involved in the innovation system producers of corn Mexico Center Region, highlighting the role of the actors of institutional support in adoption of innovations. To do this, 490 corn producers served within the framework of MasAgro 2012 program, in the states of Guerrero, Hidalgo, Puebla, Morelos, State of Mexico and Tlaxcala were interviewed. The degree of adoption of innovations was measured, and using the methodology of social network analysis linking between actors analyzed.